After the financial meltdown of the last decade, many lenders have found themselves in a tough situation where they cannot use the conventional underwriting guidelines to identify enough “good borrowers” to whom they can lend money. AI & ML are changing the way credit has been assessed and is allowing vendors to look for new credit approval metrics such as purchasing history, bank data, or social media habits. Harnessing this technology to better forecast defaults.
Fraud has been causing rising challenges for businesses. Over 72% of businesses cite fraud as a growing concern, and about 63% of businesses report the same or higher levels of fraudulent losses over the past 12 months according to a report by Experian Global. The challenge is not just about preventing fraud, but figuring out how to predict it before it happens, so it can be prevented from happening at all. But before we make strategies to combat fraud, it’s important to understand the barriers associated with fraud detection strategies.
Healthcare claims are an inviting and alluring target for fraudsters due to their huge monetary value. In 2017 alone, HHS and DoJ recovered around 2.5 Billion Dollars in claims fraud. And that is just one year.
The challenges facing healthcare payers are manifold as they try to tackle the rampant and growing problem of claims fraud. From high dollar impact, billing compliances to the high cost of manual investigations, the health care providers are sweating. So what exactly is Healthcare Fraud? And how can we try to mitigate them using technology?
Reimbursable employee expenses are a significant cost for most businesses, and unfortunately, they are often a source of fraud by unscrupulous employees. To combat this, businesses typically rely on auditing employee expense reports manually. As this is a tedious and very labor-intensive process, usually only 10% of all the receipts get audited. Thus businesses have an increased risk of expense report fraud going undetected.
With the rapid growth of the internet and the IoT and the resultant digital transformation of the world we live in, there is an explosion of data that is being generated, collected, and stored. There is data available for “normal” transactions, as well as for the fraudulent of interest to a company. If one could successfully analyze this data and gain meaningful insights and draw conclusions from it, it would be possible to use that insight for reducing the threats and risks against organizations.